Overview

Dataset statistics

Number of variables67
Number of observations11430
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 MiB
Average record size in memory536.0 B

Variable types

Categorical39
Numeric28

Warnings

ratio_nullHyperlinks has constant value "0" Constant
ratio_intRedirection has constant value "0" Constant
ratio_intErrors has constant value "0" Constant
submit_email has constant value "0" Constant
sfh has constant value "0" Constant
url has a high cardinality: 11429 distinct values High cardinality
nb_dslash is highly correlated with http_in_pathHigh correlation
http_in_path is highly correlated with nb_dslashHigh correlation
shortest_word_host is highly correlated with avg_word_hostHigh correlation
longest_words_raw is highly correlated with longest_word_path and 2 other fieldsHigh correlation
longest_word_host is highly correlated with avg_word_hostHigh correlation
longest_word_path is highly correlated with longest_words_raw and 2 other fieldsHigh correlation
avg_words_raw is highly correlated with longest_words_raw and 2 other fieldsHigh correlation
avg_word_host is highly correlated with shortest_word_host and 1 other fieldsHigh correlation
avg_word_path is highly correlated with longest_words_raw and 2 other fieldsHigh correlation
domain_in_brand is highly correlated with page_rankHigh correlation
ratio_intHyperlinks is highly correlated with ratio_extHyperlinks and 3 other fieldsHigh correlation
ratio_extHyperlinks is highly correlated with ratio_intHyperlinks and 1 other fieldsHigh correlation
external_favicon is highly correlated with ratio_extHyperlinksHigh correlation
links_in_tags is highly correlated with ratio_intHyperlinksHigh correlation
ratio_intMedia is highly correlated with ratio_intHyperlinksHigh correlation
safe_anchor is highly correlated with ratio_intHyperlinksHigh correlation
domain_age is highly correlated with page_rankHigh correlation
page_rank is highly correlated with domain_in_brand and 1 other fieldsHigh correlation
nb_www is highly correlated with char_repeatHigh correlation
length_words_raw is highly correlated with shortest_words_raw and 2 other fieldsHigh correlation
char_repeat is highly correlated with nb_wwwHigh correlation
shortest_words_raw is highly correlated with length_words_rawHigh correlation
shortest_word_host is highly correlated with avg_word_hostHigh correlation
shortest_word_path is highly correlated with longest_word_path and 1 other fieldsHigh correlation
longest_words_raw is highly correlated with longest_word_host and 3 other fieldsHigh correlation
longest_word_host is highly correlated with longest_words_raw and 2 other fieldsHigh correlation
longest_word_path is highly correlated with length_words_raw and 3 other fieldsHigh correlation
avg_words_raw is highly correlated with longest_words_raw and 2 other fieldsHigh correlation
avg_word_host is highly correlated with shortest_word_host and 3 other fieldsHigh correlation
avg_word_path is highly correlated with length_words_raw and 2 other fieldsHigh correlation
nb_hyperlinks is highly correlated with empty_titleHigh correlation
ratio_intHyperlinks is highly correlated with links_in_tags and 1 other fieldsHigh correlation
ratio_extHyperlinks is highly correlated with nb_extCSS and 2 other fieldsHigh correlation
nb_extCSS is highly correlated with ratio_extHyperlinks and 1 other fieldsHigh correlation
external_favicon is highly correlated with ratio_extHyperlinks and 1 other fieldsHigh correlation
links_in_tags is highly correlated with ratio_intHyperlinksHigh correlation
ratio_intMedia is highly correlated with ratio_intHyperlinksHigh correlation
ratio_extMedia is highly correlated with ratio_extHyperlinksHigh correlation
empty_title is highly correlated with nb_hyperlinksHigh correlation
domain_age is highly correlated with page_rankHigh correlation
page_rank is highly correlated with domain_ageHigh correlation
ip is highly correlated with nb_www and 37 other fieldsHigh correlation
nb_www is highly correlated with ip and 35 other fieldsHigh correlation
nb_com is highly correlated with ip and 37 other fieldsHigh correlation
nb_dslash is highly correlated with ip and 34 other fieldsHigh correlation
http_in_path is highly correlated with ip and 37 other fieldsHigh correlation
punycode is highly correlated with ip and 34 other fieldsHigh correlation
port is highly correlated with ip and 37 other fieldsHigh correlation
tld_in_path is highly correlated with ip and 35 other fieldsHigh correlation
tld_in_subdomain is highly correlated with ip and 37 other fieldsHigh correlation
abnormal_subdomain is highly correlated with ip and 35 other fieldsHigh correlation
nb_subdomains is highly correlated with prefix_suffix and 27 other fieldsHigh correlation
prefix_suffix is highly correlated with ip and 37 other fieldsHigh correlation
random_domain is highly correlated with ip and 38 other fieldsHigh correlation
shortening_service is highly correlated with ip and 34 other fieldsHigh correlation
path_extension is highly correlated with ip and 38 other fieldsHigh correlation
nb_redirection is highly correlated with nb_external_redirection and 21 other fieldsHigh correlation
nb_external_redirection is highly correlated with ip and 39 other fieldsHigh correlation
length_words_raw is highly correlated with shortest_words_raw and 24 other fieldsHigh correlation
char_repeat is highly correlated with phish_hints and 22 other fieldsHigh correlation
shortest_words_raw is highly correlated with length_words_raw and 22 other fieldsHigh correlation
shortest_word_host is highly correlated with nb_www and 26 other fieldsHigh correlation
shortest_word_path is highly correlated with phish_hints and 23 other fieldsHigh correlation
longest_words_raw is highly correlated with longest_word_host and 25 other fieldsHigh correlation
longest_word_host is highly correlated with longest_words_raw and 22 other fieldsHigh correlation
longest_word_path is highly correlated with avg_word_path and 24 other fieldsHigh correlation
avg_words_raw is highly correlated with longest_words_raw and 22 other fieldsHigh correlation
avg_word_host is highly correlated with longest_word_host and 23 other fieldsHigh correlation
avg_word_path is highly correlated with longest_word_path and 24 other fieldsHigh correlation
phish_hints is highly correlated with ip and 48 other fieldsHigh correlation
domain_in_brand is highly correlated with ip and 48 other fieldsHigh correlation
brand_in_subdomain is highly correlated with ip and 50 other fieldsHigh correlation
brand_in_path is highly correlated with ip and 48 other fieldsHigh correlation
suspecious_tld is highly correlated with ip and 50 other fieldsHigh correlation
statistical_report is highly correlated with ip and 49 other fieldsHigh correlation
nb_hyperlinks is highly correlated with ratio_nullHyperlinks and 14 other fieldsHigh correlation
ratio_intHyperlinks is highly correlated with ratio_extHyperlinks and 19 other fieldsHigh correlation
ratio_extHyperlinks is highly correlated with ratio_intHyperlinks and 15 other fieldsHigh correlation
ratio_nullHyperlinks is highly correlated with ip and 54 other fieldsHigh correlation
nb_extCSS is highly correlated with ip and 38 other fieldsHigh correlation
ratio_intRedirection is highly correlated with ip and 54 other fieldsHigh correlation
ratio_extRedirection is highly correlated with ratio_intErrors and 12 other fieldsHigh correlation
ratio_intErrors is highly correlated with ip and 55 other fieldsHigh correlation
ratio_extErrors is highly correlated with ip and 37 other fieldsHigh correlation
login_form is highly correlated with ip and 55 other fieldsHigh correlation
external_favicon is highly correlated with ip and 35 other fieldsHigh correlation
links_in_tags is highly correlated with submit_email and 11 other fieldsHigh correlation
submit_email is highly correlated with ip and 56 other fieldsHigh correlation
ratio_intMedia is highly correlated with submit_email and 11 other fieldsHigh correlation
ratio_extMedia is highly correlated with ratio_intHyperlinks and 16 other fieldsHigh correlation
sfh is highly correlated with ip and 57 other fieldsHigh correlation
iframe is highly correlated with ip and 57 other fieldsHigh correlation
popup_window is highly correlated with ip and 57 other fieldsHigh correlation
safe_anchor is highly correlated with onmouseover and 6 other fieldsHigh correlation
onmouseover is highly correlated with ip and 58 other fieldsHigh correlation
right_clic is highly correlated with ip and 58 other fieldsHigh correlation
empty_title is highly correlated with ip and 58 other fieldsHigh correlation
domain_in_title is highly correlated with ip and 57 other fieldsHigh correlation
domain_with_copyright is highly correlated with ip and 34 other fieldsHigh correlation
whois_registered_domain is highly correlated with ip and 58 other fieldsHigh correlation
domain_registration_length is highly correlated with dns_record and 1 other fieldsHigh correlation
domain_age is highly correlated with dns_record and 1 other fieldsHigh correlation
web_traffic is highly correlated with dns_record and 1 other fieldsHigh correlation
dns_record is highly correlated with ip and 61 other fieldsHigh correlation
google_index is highly correlated with nb_www and 24 other fieldsHigh correlation
ratio_extHyperlinks is highly correlated with links_in_tags and 5 other fieldsHigh correlation
nb_external_redirection is highly correlated with abnormal_subdomain and 2 other fieldsHigh correlation
links_in_tags is highly correlated with ratio_extHyperlinks and 4 other fieldsHigh correlation
ratio_extMedia is highly correlated with ratio_extHyperlinks and 2 other fieldsHigh correlation
longest_words_raw is highly correlated with longest_word_path and 3 other fieldsHigh correlation
longest_word_path is highly correlated with longest_words_raw and 3 other fieldsHigh correlation
shortest_word_host is highly correlated with nb_www and 3 other fieldsHigh correlation
abnormal_subdomain is highly correlated with nb_external_redirection and 1 other fieldsHigh correlation
domain_in_title is highly correlated with statusHigh correlation
status is highly correlated with domain_in_title and 3 other fieldsHigh correlation
nb_subdomains is highly correlated with nb_wwwHigh correlation
length_words_raw is highly correlated with nb_external_redirection and 6 other fieldsHigh correlation
safe_anchor is highly correlated with ratio_extHyperlinks and 3 other fieldsHigh correlation
nb_www is highly correlated with shortest_word_host and 1 other fieldsHigh correlation
avg_word_path is highly correlated with longest_words_raw and 2 other fieldsHigh correlation
ratio_intHyperlinks is highly correlated with ratio_extHyperlinks and 6 other fieldsHigh correlation
longest_word_host is highly correlated with shortest_word_host and 1 other fieldsHigh correlation
empty_title is highly correlated with links_in_tags and 2 other fieldsHigh correlation
web_traffic is highly correlated with tld_in_subdomainHigh correlation
char_repeat is highly correlated with longest_words_raw and 2 other fieldsHigh correlation
shortest_words_raw is highly correlated with shortest_word_host and 1 other fieldsHigh correlation
dns_record is highly correlated with nb_external_redirection and 1 other fieldsHigh correlation
http_in_path is highly correlated with length_words_raw and 2 other fieldsHigh correlation
page_rank is highly correlated with status and 3 other fieldsHigh correlation
ratio_intMedia is highly correlated with ratio_extHyperlinks and 3 other fieldsHigh correlation
avg_words_raw is highly correlated with longest_words_raw and 2 other fieldsHigh correlation
domain_age is highly correlated with page_rank and 1 other fieldsHigh correlation
phish_hints is highly correlated with length_words_raw and 2 other fieldsHigh correlation
avg_word_host is highly correlated with shortest_word_host and 2 other fieldsHigh correlation
google_index is highly correlated with status and 1 other fieldsHigh correlation
ip is highly correlated with length_words_raw and 1 other fieldsHigh correlation
tld_in_subdomain is highly correlated with web_traffic and 1 other fieldsHigh correlation
external_favicon is highly correlated with ratio_extHyperlinks and 2 other fieldsHigh correlation
domain_in_brand is highly correlated with page_rank and 1 other fieldsHigh correlation
nb_com is highly correlated with length_words_raw and 2 other fieldsHigh correlation
brand_in_path is highly correlated with length_words_rawHigh correlation
nb_external_redirection is highly correlated with submit_email and 4 other fieldsHigh correlation
iframe is highly correlated with submit_email and 4 other fieldsHigh correlation
onmouseover is highly correlated with submit_email and 4 other fieldsHigh correlation
submit_email is highly correlated with nb_external_redirection and 36 other fieldsHigh correlation
port is highly correlated with submit_email and 4 other fieldsHigh correlation
popup_window is highly correlated with submit_email and 4 other fieldsHigh correlation
abnormal_subdomain is highly correlated with submit_email and 4 other fieldsHigh correlation
sfh is highly correlated with nb_external_redirection and 36 other fieldsHigh correlation
domain_in_title is highly correlated with submit_email and 4 other fieldsHigh correlation
domain_with_copyright is highly correlated with submit_email and 4 other fieldsHigh correlation
status is highly correlated with submit_email and 5 other fieldsHigh correlation
path_extension is highly correlated with submit_email and 4 other fieldsHigh correlation
nb_subdomains is highly correlated with submit_email and 4 other fieldsHigh correlation
tld_in_path is highly correlated with submit_email and 4 other fieldsHigh correlation
statistical_report is highly correlated with submit_email and 4 other fieldsHigh correlation
nb_www is highly correlated with submit_email and 4 other fieldsHigh correlation
punycode is highly correlated with submit_email and 4 other fieldsHigh correlation
random_domain is highly correlated with submit_email and 4 other fieldsHigh correlation
empty_title is highly correlated with submit_email and 4 other fieldsHigh correlation
ratio_nullHyperlinks is highly correlated with nb_external_redirection and 36 other fieldsHigh correlation
dns_record is highly correlated with submit_email and 4 other fieldsHigh correlation
right_clic is highly correlated with submit_email and 4 other fieldsHigh correlation
http_in_path is highly correlated with submit_email and 5 other fieldsHigh correlation
whois_registered_domain is highly correlated with submit_email and 4 other fieldsHigh correlation
google_index is highly correlated with submit_email and 5 other fieldsHigh correlation
ip is highly correlated with submit_email and 4 other fieldsHigh correlation
tld_in_subdomain is highly correlated with submit_email and 4 other fieldsHigh correlation
external_favicon is highly correlated with submit_email and 4 other fieldsHigh correlation
ratio_intRedirection is highly correlated with nb_external_redirection and 36 other fieldsHigh correlation
brand_in_subdomain is highly correlated with submit_email and 4 other fieldsHigh correlation
nb_dslash is highly correlated with submit_email and 5 other fieldsHigh correlation
suspecious_tld is highly correlated with submit_email and 4 other fieldsHigh correlation
prefix_suffix is highly correlated with submit_email and 4 other fieldsHigh correlation
ratio_intErrors is highly correlated with nb_external_redirection and 36 other fieldsHigh correlation
brand_in_path is highly correlated with submit_email and 4 other fieldsHigh correlation
login_form is highly correlated with submit_email and 4 other fieldsHigh correlation
domain_in_brand is highly correlated with submit_email and 4 other fieldsHigh correlation
shortening_service is highly correlated with submit_email and 4 other fieldsHigh correlation
nb_extCSS is highly skewed (γ1 = 23.49547911) Skewed
url is uniformly distributed Uniform
status is uniformly distributed Uniform
nb_com has 10103 (88.4%) zeros Zeros
nb_redirection has 6775 (59.3%) zeros Zeros
char_repeat has 2361 (20.7%) zeros Zeros
shortest_word_path has 3215 (28.1%) zeros Zeros
longest_word_path has 3215 (28.1%) zeros Zeros
avg_word_path has 3215 (28.1%) zeros Zeros
phish_hints has 9389 (82.1%) zeros Zeros
nb_hyperlinks has 1381 (12.1%) zeros Zeros
ratio_intHyperlinks has 1886 (16.5%) zeros Zeros
ratio_extHyperlinks has 3071 (26.9%) zeros Zeros
nb_extCSS has 7828 (68.5%) zeros Zeros
ratio_extRedirection has 6143 (53.7%) zeros Zeros
ratio_extErrors has 8121 (71.0%) zeros Zeros
links_in_tags has 3403 (29.8%) zeros Zeros
ratio_intMedia has 5469 (47.8%) zeros Zeros
ratio_extMedia has 7335 (64.2%) zeros Zeros
safe_anchor has 4438 (38.8%) zeros Zeros
domain_registration_length has 1404 (12.3%) zeros Zeros
web_traffic has 4444 (38.9%) zeros Zeros
page_rank has 2666 (23.3%) zeros Zeros

Reproduction

Analysis started2022-01-23 22:41:12.850070
Analysis finished2022-01-23 22:47:42.235718
Duration6 minutes and 29.39 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

url
Categorical

HIGH CARDINALITY
UNIFORM

Distinct11429
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
http://e710z0ear.du.r.appspot.com/c:/users/user/downlo
 
2
http://www.crestonwood.com/router.php
 
1
https://www.riverbed.com/products/steelcentral/network-performance-management/steelcentral-packet-analyzer.html
 
1
http://fience.vot.pl/xl
 
1
https://usbank.app.link/NquAmzCW01?platform=hootsuite
 
1
Other values (11424)
11424 

Length

Max length1641
Median length47
Mean length61.120035
Min length12

Characters and Unicode

Total characters698602
Distinct characters100
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11428 ?
Unique (%)> 99.9%

Sample

1st rowhttp://www.crestonwood.com/router.php
2nd rowhttp://shadetreetechnology.com/V4/validation/a111aedc8ae390eabcfa130e041a10a4
3rd rowhttps://support-appleld.com.secureupdate.duilawyeryork.com/ap/89e6a3b4b063b8d/?cmd=_update&dispatch=89e6a3b4b063b8d1b&locale=_
4th rowhttp://rgipt.ac.in
5th rowhttp://www.iracing.com/tracks/gateway-motorsports-park/

Common Values

ValueCountFrequency (%)
http://e710z0ear.du.r.appspot.com/c:/users/user/downlo2
 
< 0.1%
http://www.crestonwood.com/router.php1
 
< 0.1%
https://www.riverbed.com/products/steelcentral/network-performance-management/steelcentral-packet-analyzer.html1
 
< 0.1%
http://fience.vot.pl/xl1
 
< 0.1%
https://usbank.app.link/NquAmzCW01?platform=hootsuite1
 
< 0.1%
http://103.229.125.10/1
 
< 0.1%
https://en.wikiquote.org/wiki/India1
 
< 0.1%
http://chasebank.com66.henrybakercollege.edu.in/chase.com/update.php1
 
< 0.1%
http://searchwindevelopment.techtarget.com/definition/domain-name1
 
< 0.1%
http://connxupdate.be/myebranch.iccu.com/Idaho%20Central%20Credit%20Union.php1
 
< 0.1%
Other values (11419)11419
99.9%

Length

2022-01-23T16:47:42.491559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http://stolizaparketa.ru/wp-content/themes/twentyfifteen/css/read/chinavali/index.php?email=_xxx@yyy.com7
 
0.1%
http://153284594738391.statictab.com/25060803
 
< 0.1%
http://himatif.pelitabangsa.ac.id/admin2
 
< 0.1%
https://623112j4j3.codesandbox.io/xrr2
 
< 0.1%
http://ijikc.co.in/sites/ijikc20/rating/ken7xx7y0p9/wemail_al.html/error.php?l2
 
< 0.1%
http://stolizaparketa.ru/wp-content/themes/twentyfifteen/css/read/chinavali/index.php?email=abuse@activesolutions.co.uk2
 
< 0.1%
http://jurnal.stai-aljawami.ac.id/.well-known/pki-validation/emoji/main/main/doc/hub2
 
< 0.1%
http://courgeon-immobilier.fr/wp-content/uploads/2019/02/tpg2
 
< 0.1%
http://zabor-vn.com/system/csvprice_pro/smart/customer_center/customer-idpp00c354/myaccount/signin2
 
< 0.1%
https://www.zabor-vn.com/system/csvprice_pro/smart/customer_center/customer-idpp00c354/myaccount/signin2
 
< 0.1%
Other values (11244)11407
99.8%

Most occurring characters

ValueCountFrequency (%)
t49171
 
7.0%
/49030
 
7.0%
e40829
 
5.8%
o35303
 
5.1%
a32792
 
4.7%
p30923
 
4.4%
s29208
 
4.2%
c28485
 
4.1%
.28354
 
4.1%
i27617
 
4.0%
Other values (90)346890
49.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter492123
70.4%
Other Punctuation95110
 
13.6%
Decimal Number62318
 
8.9%
Uppercase Letter30171
 
4.3%
Dash Punctuation11402
 
1.6%
Connector Punctuation3688
 
0.5%
Math Symbol3552
 
0.5%
Control65
 
< 0.1%
Open Punctuation64
 
< 0.1%
Close Punctuation63
 
< 0.1%
Other values (4)46
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1951
 
6.5%
D1674
 
5.5%
S1508
 
5.0%
C1502
 
5.0%
F1499
 
5.0%
E1399
 
4.6%
B1368
 
4.5%
N1287
 
4.3%
T1274
 
4.2%
M1266
 
4.2%
Other values (18)15443
51.2%
Lowercase Letter
ValueCountFrequency (%)
t49171
 
10.0%
e40829
 
8.3%
o35303
 
7.2%
a32792
 
6.7%
p30923
 
6.3%
s29208
 
5.9%
c28485
 
5.8%
i27617
 
5.6%
r23269
 
4.7%
n22460
 
4.6%
Other values (17)172066
35.0%
Other Punctuation
ValueCountFrequency (%)
/49030
51.6%
.28354
29.8%
:11749
 
12.4%
&1855
 
2.0%
?1614
 
1.7%
%1407
 
1.5%
;712
 
0.7%
@254
 
0.3%
#50
 
0.1%
,46
 
< 0.1%
Other values (3)39
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
28092
13.0%
07889
12.7%
17375
11.8%
36154
9.9%
45713
9.2%
75622
9.0%
55578
9.0%
65386
8.6%
85319
8.5%
95190
8.3%
Math Symbol
ValueCountFrequency (%)
=3351
94.3%
+120
 
3.4%
~78
 
2.2%
<3
 
0.1%
Open Punctuation
ValueCountFrequency (%)
(45
70.3%
[10
 
15.6%
{9
 
14.1%
Close Punctuation
ValueCountFrequency (%)
)45
71.4%
]10
 
15.9%
}8
 
12.7%
Control
ValueCountFrequency (%)
‚33
50.8%
ƒ31
47.7%
‘1
 
1.5%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Modifier Symbol
ValueCountFrequency (%)
`17
89.5%
^2
 
10.5%
Space Separator
ValueCountFrequency (%)
 2
66.7%
1
33.3%
Dash Punctuation
ValueCountFrequency (%)
-11402
100.0%
Connector Punctuation
ValueCountFrequency (%)
_3688
100.0%
Currency Symbol
ValueCountFrequency (%)
$22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin522293
74.8%
Common176307
 
25.2%
Han2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t49171
 
9.4%
e40829
 
7.8%
o35303
 
6.8%
a32792
 
6.3%
p30923
 
5.9%
s29208
 
5.6%
c28485
 
5.5%
i27617
 
5.3%
r23269
 
4.5%
n22460
 
4.3%
Other values (44)202236
38.7%
Common
ValueCountFrequency (%)
/49030
27.8%
.28354
16.1%
:11749
 
6.7%
-11402
 
6.5%
28092
 
4.6%
07889
 
4.5%
17375
 
4.2%
36154
 
3.5%
45713
 
3.2%
75622
 
3.2%
Other values (34)34927
19.8%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII698466
> 99.9%
Latin 1 Sup134
 
< 0.1%
CJK2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t49171
 
7.0%
/49030
 
7.0%
e40829
 
5.8%
o35303
 
5.1%
a32792
 
4.7%
p30923
 
4.4%
s29208
 
4.2%
c28485
 
4.1%
.28354
 
4.1%
i27617
 
4.0%
Other values (81)346754
49.6%
Latin 1 Sup
ValueCountFrequency (%)
Ã33
24.6%
Â33
24.6%
‚33
24.6%
ƒ31
23.1%
 2
 
1.5%
µ1
 
0.7%
‘1
 
0.7%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

ip
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
9709 
1
1721 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

Length

2022-01-23T16:47:42.705334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:42.749960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

Most occurring characters

ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

nb_www
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
6330 
1
5074 
2
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

Length

2022-01-23T16:47:43.171107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:43.228488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

Most occurring characters

ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

nb_com
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1279965004
Minimum0
Maximum6
Zeros10103
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:43.287805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3790078643
Coefficient of variation (CV)2.961079897
Kurtosis23.44330347
Mean0.1279965004
Median Absolute Deviation (MAD)0
Skewness3.778379347
Sum1463
Variance0.1436469612
MonotonicityNot monotonic
2022-01-23T16:47:43.362042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
010103
88.4%
11220
 
10.7%
289
 
0.8%
312
 
0.1%
43
 
< 0.1%
62
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
010103
88.4%
11220
 
10.7%
289
 
0.8%
312
 
0.1%
43
 
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
51
 
< 0.1%
43
 
< 0.1%
312
 
0.1%
289
 
0.8%
11220
 
10.7%
010103
88.4%

nb_dslash
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11355 
1
 
75

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

Length

2022-01-23T16:47:43.535477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:43.594393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

Most occurring characters

ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

http_in_path
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11280 
1
 
129
2
 
10
4
 
9
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

Length

2022-01-23T16:47:43.750856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:43.811773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

punycode
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11426 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

Length

2022-01-23T16:47:43.975597image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:44.036564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

port
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11403 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

Length

2022-01-23T16:47:44.187156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:44.244530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

Most occurring characters

ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

tld_in_path
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10680 
1
 
750

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

Length

2022-01-23T16:47:44.393081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:44.452239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

Most occurring characters

ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

tld_in_subdomain
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10857 
1
 
573

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

Length

2022-01-23T16:47:44.593955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:44.650133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

Most occurring characters

ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

abnormal_subdomain
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11183 
1
 
247

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

Length

2022-01-23T16:47:44.799070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:44.857229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

Most occurring characters

ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

nb_subdomains
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
2
6178 
3
3950 
1
1302 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

Length

2022-01-23T16:47:45.000295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:45.062038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

Most occurring characters

ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

prefix_suffix
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
9116 
1
2314 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

Length

2022-01-23T16:47:45.223733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:45.284702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

Most occurring characters

ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

random_domain
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10478 
1
 
952

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

Length

2022-01-23T16:47:45.432803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:45.493224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

Most occurring characters

ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

shortening_service
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10019 
1
1411 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

Length

2022-01-23T16:47:45.640114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:45.697621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

Most occurring characters

ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

path_extension
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11428 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

Length

2022-01-23T16:47:45.842653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:45.901342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

nb_redirection
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4982502187
Minimum0
Maximum6
Zeros6775
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:45.956283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6919069557
Coefficient of variation (CV)1.388673662
Kurtosis3.524335378
Mean0.4982502187
Median Absolute Deviation (MAD)0
Skewness1.568452476
Sum5695
Variance0.4787352353
MonotonicityNot monotonic
2022-01-23T16:47:46.036636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
06775
59.3%
13827
33.5%
2662
 
5.8%
3128
 
1.1%
431
 
0.3%
56
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
06775
59.3%
13827
33.5%
2662
 
5.8%
3128
 
1.1%
431
 
0.3%
56
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
56
 
0.1%
431
 
0.3%
3128
 
1.1%
2662
 
5.8%
13827
33.5%
06775
59.3%

nb_external_redirection
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11394 
1
 
36

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

Length

2022-01-23T16:47:46.209178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:46.268204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

Most occurring characters

ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

length_words_raw
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.232808399
Minimum1
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:46.347632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median5
Q38
95-th percentile14
Maximum106
Range105
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.572355118
Coefficient of variation (CV)0.8940360045
Kurtosis60.7624273
Mean6.232808399
Median Absolute Deviation (MAD)3
Skewness5.36734996
Sum71241
Variance31.05114156
MonotonicityNot monotonic
2022-01-23T16:47:46.478217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22556
22.4%
41157
10.1%
51094
9.6%
61088
9.5%
31080
9.4%
7823
 
7.2%
8647
 
5.7%
9498
 
4.4%
10419
 
3.7%
13370
 
3.2%
Other values (44)1698
14.9%
ValueCountFrequency (%)
1354
 
3.1%
22556
22.4%
31080
9.4%
41157
10.1%
51094
9.6%
61088
9.5%
7823
 
7.2%
8647
 
5.7%
9498
 
4.4%
10419
 
3.7%
ValueCountFrequency (%)
1061
 
< 0.1%
962
 
< 0.1%
902
 
< 0.1%
811
 
< 0.1%
806
0.1%
681
 
< 0.1%
644
< 0.1%
631
 
< 0.1%
612
 
< 0.1%
601
 
< 0.1%

char_repeat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct55
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.927471566
Minimum0
Maximum146
Zeros2361
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:46.602357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile7
Maximum146
Range146
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.768935795
Coefficient of variation (CV)1.629028903
Kurtosis393.756615
Mean2.927471566
Median Absolute Deviation (MAD)2
Skewness15.75678114
Sum33461
Variance22.74274862
MonotonicityNot monotonic
2022-01-23T16:47:46.724808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33148
27.5%
02361
20.7%
11712
15.0%
41677
14.7%
2875
 
7.7%
5679
 
5.9%
6381
 
3.3%
7164
 
1.4%
8123
 
1.1%
960
 
0.5%
Other values (45)250
 
2.2%
ValueCountFrequency (%)
02361
20.7%
11712
15.0%
2875
 
7.7%
33148
27.5%
41677
14.7%
5679
 
5.9%
6381
 
3.3%
7164
 
1.4%
8123
 
1.1%
960
 
0.5%
ValueCountFrequency (%)
1462
< 0.1%
1451
< 0.1%
1441
< 0.1%
1411
< 0.1%
1011
< 0.1%
961
< 0.1%
851
< 0.1%
661
< 0.1%
611
< 0.1%
592
< 0.1%

shortest_words_raw
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.127296588
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:46.840460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile8
Maximum31
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.211571305
Coefficient of variation (CV)0.7071831031
Kurtosis15.7258904
Mean3.127296588
Median Absolute Deviation (MAD)1
Skewness3.156658185
Sum35745
Variance4.891047639
MonotonicityNot monotonic
2022-01-23T16:47:46.936381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
34730
41.4%
23112
27.2%
11522
 
13.3%
4631
 
5.5%
8411
 
3.6%
5265
 
2.3%
6264
 
2.3%
7160
 
1.4%
9102
 
0.9%
1157
 
0.5%
Other values (15)176
 
1.5%
ValueCountFrequency (%)
11522
 
13.3%
23112
27.2%
34730
41.4%
4631
 
5.5%
5265
 
2.3%
6264
 
2.3%
7160
 
1.4%
8411
 
3.6%
9102
 
0.9%
1044
 
0.4%
ValueCountFrequency (%)
311
 
< 0.1%
271
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
214
 
< 0.1%
206
0.1%
196
0.1%
186
0.1%
177
0.1%
1613
0.1%

shortest_word_host
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.019772528
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:47.040456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q36
95-th percentile13
Maximum39
Range38
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.94157981
Coefficient of variation (CV)0.7852108412
Kurtosis6.957438136
Mean5.019772528
Median Absolute Deviation (MAD)0
Skewness2.267936107
Sum57376
Variance15.5360514
MonotonicityNot monotonic
2022-01-23T16:47:47.155758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
35745
50.3%
2873
 
7.6%
8634
 
5.5%
4601
 
5.3%
5556
 
4.9%
6474
 
4.1%
7439
 
3.8%
1408
 
3.6%
9322
 
2.8%
10285
 
2.5%
Other values (24)1093
 
9.6%
ValueCountFrequency (%)
1408
 
3.6%
2873
 
7.6%
35745
50.3%
4601
 
5.3%
5556
 
4.9%
6474
 
4.1%
7439
 
3.8%
8634
 
5.5%
9322
 
2.8%
10285
 
2.5%
ValueCountFrequency (%)
394
< 0.1%
381
 
< 0.1%
331
 
< 0.1%
312
< 0.1%
302
< 0.1%
293
< 0.1%
282
< 0.1%
271
 
< 0.1%
263
< 0.1%
254
< 0.1%

shortest_word_path
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.398950131
Minimum0
Maximum40
Zeros3215
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:47.268759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile7
Maximum40
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.997808681
Coefficient of variation (CV)1.249633597
Kurtosis38.27818007
Mean2.398950131
Median Absolute Deviation (MAD)1
Skewness4.687603636
Sum27420
Variance8.986856891
MonotonicityNot monotonic
2022-01-23T16:47:47.376799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
03215
28.1%
22617
22.9%
31941
17.0%
41258
 
11.0%
11162
 
10.2%
5354
 
3.1%
6297
 
2.6%
7158
 
1.4%
8105
 
0.9%
1074
 
0.6%
Other values (23)249
 
2.2%
ValueCountFrequency (%)
03215
28.1%
11162
 
10.2%
22617
22.9%
31941
17.0%
41258
 
11.0%
5354
 
3.1%
6297
 
2.6%
7158
 
1.4%
8105
 
0.9%
966
 
0.6%
ValueCountFrequency (%)
402
 
< 0.1%
372
 
< 0.1%
361
 
< 0.1%
3228
0.2%
301
 
< 0.1%
292
 
< 0.1%
283
 
< 0.1%
261
 
< 0.1%
241
 
< 0.1%
233
 
< 0.1%

longest_words_raw
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct119
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.39387577
Minimum2
Maximum829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:47.500798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q19
median11
Q316
95-th percentile32
Maximum829
Range827
Interquartile range (IQR)7

Descriptive statistics

Standard deviation22.0836445
Coefficient of variation (CV)1.434573387
Kurtosis294.7805804
Mean15.39387577
Median Absolute Deviation (MAD)3
Skewness13.53102354
Sum175952
Variance487.6873542
MonotonicityNot monotonic
2022-01-23T16:47:47.624423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91163
 
10.2%
101143
 
10.0%
11983
 
8.6%
8951
 
8.3%
12823
 
7.2%
13726
 
6.4%
7700
 
6.1%
17634
 
5.5%
14554
 
4.8%
15514
 
4.5%
Other values (109)3239
28.3%
ValueCountFrequency (%)
25
 
< 0.1%
3106
 
0.9%
4135
 
1.2%
5286
 
2.5%
6416
 
3.6%
7700
6.1%
8951
8.3%
91163
10.2%
101143
10.0%
11983
8.6%
ValueCountFrequency (%)
8291
 
< 0.1%
5071
 
< 0.1%
4921
 
< 0.1%
4871
 
< 0.1%
4061
 
< 0.1%
3833
 
< 0.1%
3013
 
< 0.1%
3003
 
< 0.1%
28810
0.1%
2401
 
< 0.1%

longest_word_host
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.467979
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:47.746681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median10
Q313
95-th percentile19
Maximum62
Range61
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.932014659
Coefficient of variation (CV)0.4711525174
Kurtosis7.201449994
Mean10.467979
Median Absolute Deviation (MAD)3
Skewness1.631076325
Sum119649
Variance24.3247686
MonotonicityNot monotonic
2022-01-23T16:47:47.873764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
91176
10.3%
81061
 
9.3%
71034
 
9.0%
10994
 
8.7%
13970
 
8.5%
6910
 
8.0%
11836
 
7.3%
12691
 
6.0%
5581
 
5.1%
14520
 
4.5%
Other values (39)2657
23.2%
ValueCountFrequency (%)
116
 
0.1%
237
 
0.3%
3344
 
3.0%
4378
 
3.3%
5581
5.1%
6910
8.0%
71034
9.0%
81061
9.3%
91176
10.3%
10994
8.7%
ValueCountFrequency (%)
621
 
< 0.1%
611
 
< 0.1%
601
 
< 0.1%
541
 
< 0.1%
471
 
< 0.1%
442
< 0.1%
432
< 0.1%
423
< 0.1%
411
 
< 0.1%
401
 
< 0.1%

longest_word_path
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct120
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.56150481
Minimum0
Maximum829
Zeros3215
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:48.002665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q311
95-th percentile32
Maximum829
Range829
Interquartile range (IQR)11

Descriptive statistics

Standard deviation23.07788337
Coefficient of variation (CV)2.185094244
Kurtosis256.859316
Mean10.56150481
Median Absolute Deviation (MAD)5
Skewness12.39572976
Sum120718
Variance532.5887009
MonotonicityNot monotonic
2022-01-23T16:47:48.128694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03215
28.1%
7983
 
8.6%
8930
 
8.1%
10794
 
6.9%
9747
 
6.5%
6539
 
4.7%
5538
 
4.7%
11468
 
4.1%
17405
 
3.5%
32380
 
3.3%
Other values (110)2431
21.3%
ValueCountFrequency (%)
03215
28.1%
141
 
0.4%
2130
 
1.1%
3157
 
1.4%
4306
 
2.7%
5538
 
4.7%
6539
 
4.7%
7983
 
8.6%
8930
 
8.1%
9747
 
6.5%
ValueCountFrequency (%)
8291
 
< 0.1%
5071
 
< 0.1%
4921
 
< 0.1%
4871
 
< 0.1%
4061
 
< 0.1%
3833
 
< 0.1%
3013
 
< 0.1%
3003
 
< 0.1%
28810
0.1%
2401
 
< 0.1%

avg_words_raw
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct896
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.258881692
Minimum2
Maximum128.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:48.249665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15.25
median6.5
Q38
95-th percentile13.2
Maximum128.25
Range126.25
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation4.145826506
Coefficient of variation (CV)0.5711384593
Kurtosis189.4561432
Mean7.258881692
Median Absolute Deviation (MAD)1.5
Skewness9.548721745
Sum82969.01774
Variance17.18787742
MonotonicityNot monotonic
2022-01-23T16:47:48.382026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6536
 
4.7%
5494
 
4.3%
7465
 
4.1%
8369
 
3.2%
6.5342
 
3.0%
5.5317
 
2.8%
9255
 
2.2%
4245
 
2.1%
4.5238
 
2.1%
7.5234
 
2.0%
Other values (886)7935
69.4%
ValueCountFrequency (%)
214
0.1%
2.1428571431
 
< 0.1%
2.2352941181
 
< 0.1%
2.253
 
< 0.1%
2.2631578951
 
< 0.1%
2.2926829271
 
< 0.1%
2.32
 
< 0.1%
2.3333333332
 
< 0.1%
2.3751
 
< 0.1%
2.4285714293
 
< 0.1%
ValueCountFrequency (%)
128.251
 
< 0.1%
106.51
 
< 0.1%
1003
< 0.1%
83.363636361
 
< 0.1%
741
 
< 0.1%
68.1251
 
< 0.1%
612
< 0.1%
47.251
 
< 0.1%
45.752
< 0.1%
42.6251
 
< 0.1%

avg_word_host
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct174
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.678074522
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:48.513931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5
Q15.25
median7
Q39
95-th percentile14.5
Maximum39
Range38
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation3.578434671
Coefficient of variation (CV)0.4660588616
Kurtosis5.93341247
Mean7.678074522
Median Absolute Deviation (MAD)2
Skewness1.746079562
Sum87760.39179
Variance12.80519469
MonotonicityNot monotonic
2022-01-23T16:47:48.645595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5845
 
7.4%
5.5828
 
7.2%
6796
 
7.0%
7765
 
6.7%
8665
 
5.8%
6.5573
 
5.0%
4504
 
4.4%
9481
 
4.2%
7.5474
 
4.1%
4.5461
 
4.0%
Other values (164)5038
44.1%
ValueCountFrequency (%)
116
 
0.1%
1.51
 
< 0.1%
1.6666666677
 
0.1%
1.752
 
< 0.1%
239
0.3%
2.2518
 
0.2%
2.3333333336
 
0.1%
2.561
0.5%
2.61
 
< 0.1%
2.66666666710
 
0.1%
ValueCountFrequency (%)
394
< 0.1%
381
 
< 0.1%
331
 
< 0.1%
312
< 0.1%
302
< 0.1%
29.51
 
< 0.1%
293
< 0.1%
282
< 0.1%
271
 
< 0.1%
263
< 0.1%

avg_word_path
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct757
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.092424702
Minimum0
Maximum250
Zeros3215
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:48.788926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.857142857
Q36.714285714
95-th percentile13
Maximum250
Range250
Interquartile range (IQR)6.714285714

Descriptive statistics

Standard deviation7.14704972
Coefficient of variation (CV)1.403466941
Kurtosis336.1082963
Mean5.092424702
Median Absolute Deviation (MAD)2.476190476
Skewness13.44674137
Sum58206.41434
Variance51.0803197
MonotonicityNot monotonic
2022-01-23T16:47:48.924412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03215
28.1%
5470
 
4.1%
6406
 
3.6%
4397
 
3.5%
7266
 
2.3%
7.375217
 
1.9%
4.5215
 
1.9%
5.5209
 
1.8%
3189
 
1.7%
6.5159
 
1.4%
Other values (747)5687
49.8%
ValueCountFrequency (%)
03215
28.1%
141
 
0.4%
1.53
 
< 0.1%
1.6666666672
 
< 0.1%
2140
 
1.2%
2.0512820511
 
< 0.1%
2.1111111111
 
< 0.1%
2.1666666671
 
< 0.1%
2.2363636361
 
< 0.1%
2.254
 
< 0.1%
ValueCountFrequency (%)
2501
 
< 0.1%
2061
 
< 0.1%
194.53
< 0.1%
118.52
< 0.1%
115.41
 
< 0.1%
103.51
 
< 0.1%
96.222222221
 
< 0.1%
86.833333331
 
< 0.1%
86.53
< 0.1%
72.53
< 0.1%

phish_hints
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3277340332
Minimum0
Maximum10
Zeros9389
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:49.036074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.842600352
Coefficient of variation (CV)2.57098826
Kurtosis12.5187366
Mean0.3277340332
Median Absolute Deviation (MAD)0
Skewness3.216484397
Sum3746
Variance0.7099753532
MonotonicityNot monotonic
2022-01-23T16:47:49.124119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
09389
82.1%
11079
 
9.4%
2460
 
4.0%
3325
 
2.8%
4136
 
1.2%
528
 
0.2%
69
 
0.1%
102
 
< 0.1%
72
 
< 0.1%
ValueCountFrequency (%)
09389
82.1%
11079
 
9.4%
2460
 
4.0%
3325
 
2.8%
4136
 
1.2%
528
 
0.2%
69
 
0.1%
72
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
102
 
< 0.1%
72
 
< 0.1%
69
 
0.1%
528
 
0.2%
4136
 
1.2%
3325
 
2.8%
2460
 
4.0%
11079
 
9.4%
09389
82.1%

domain_in_brand
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10239 
1
1191 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

Length

2022-01-23T16:47:49.309970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:49.370976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

Most occurring characters

ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

brand_in_subdomain
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11383 
1
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

Length

2022-01-23T16:47:49.524493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:49.585409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

Most occurring characters

ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

brand_in_path
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11374 
1
 
56

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

Length

2022-01-23T16:47:49.734488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:49.793098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

Most occurring characters

ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

suspecious_tld
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11225 
1
 
205

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

Length

2022-01-23T16:47:49.952945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:50.013768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

Most occurring characters

ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

statistical_report
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11053 
2
 
306
1
 
71

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

Length

2022-01-23T16:47:50.175581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:50.236409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

Most occurring characters

ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

nb_hyperlinks
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct691
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.18976378
Minimum0
Maximum4659
Zeros1381
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:50.317366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median34
Q3101
95-th percentile323
Maximum4659
Range4659
Interquartile range (IQR)92

Descriptive statistics

Standard deviation166.7582535
Coefficient of variation (CV)1.912589807
Kurtosis117.9666629
Mean87.18976378
Median Absolute Deviation (MAD)32
Skewness7.675060655
Sum996579
Variance27808.31511
MonotonicityNot monotonic
2022-01-23T16:47:50.443410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01381
 
12.1%
4344
 
3.0%
51332
 
2.9%
1328
 
2.9%
14258
 
2.3%
21206
 
1.8%
22189
 
1.7%
7171
 
1.5%
8166
 
1.5%
6143
 
1.3%
Other values (681)7912
69.2%
ValueCountFrequency (%)
01381
12.1%
1328
 
2.9%
263
 
0.6%
3113
 
1.0%
4344
 
3.0%
5141
 
1.2%
6143
 
1.3%
7171
 
1.5%
8166
 
1.5%
9122
 
1.1%
ValueCountFrequency (%)
46591
< 0.1%
38221
< 0.1%
31481
< 0.1%
29351
< 0.1%
27261
< 0.1%
22281
< 0.1%
22051
< 0.1%
21691
< 0.1%
19051
< 0.1%
19031
< 0.1%

ratio_intHyperlinks
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3131
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6024572495
Minimum0
Maximum1
Zeros1886
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:51.462762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2249909387
median0.7434423815
Q30.9447668415
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.7197759027

Descriptive statistics

Standard deviation0.3764744871
Coefficient of variation (CV)0.6248982603
Kurtosis-1.297591151
Mean0.6024572495
Median Absolute Deviation (MAD)0.2523019915
Skewness-0.5280091394
Sum6886.086362
Variance0.1417330395
MonotonicityNot monotonic
2022-01-23T16:47:51.625493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01886
 
16.5%
11690
 
14.8%
0.071428571184
 
1.6%
0.5126
 
1.1%
0.19047619113
 
1.0%
0.66666666798
 
0.9%
0.85714285798
 
0.9%
0.33333333397
 
0.8%
0.7580
 
0.7%
0.18181818266
 
0.6%
Other values (3121)6992
61.2%
ValueCountFrequency (%)
01886
16.5%
0.0088495581
 
< 0.1%
0.0098039221
 
< 0.1%
0.0119284295
 
< 0.1%
0.0156251
 
< 0.1%
0.0164410061
 
< 0.1%
0.0166666671
 
< 0.1%
0.0172413791
 
< 0.1%
0.0210084031
 
< 0.1%
0.0212765961
 
< 0.1%
ValueCountFrequency (%)
11690
14.8%
0.9993593851
 
< 0.1%
0.9984615381
 
< 0.1%
0.9977957381
 
< 0.1%
0.9976726141
 
< 0.1%
0.9972652691
 
< 0.1%
0.996908811
 
< 0.1%
0.996870111
 
< 0.1%
0.9968652041
 
< 0.1%
0.9968025581
 
< 0.1%

ratio_extHyperlinks
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3131
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2767203533
Minimum0
Maximum1
Zeros3071
Zeros (%)26.9%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:51.759881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.131147541
Q30.4748397435
95-th percentile0.961538462
Maximum1
Range1
Interquartile range (IQR)0.4748397435

Descriptive statistics

Standard deviation0.3199582523
Coefficient of variation (CV)1.156251243
Kurtosis-0.3202850408
Mean0.2767203533
Median Absolute Deviation (MAD)0.131147541
Skewness1.008455407
Sum3162.913638
Variance0.1023732832
MonotonicityNot monotonic
2022-01-23T16:47:51.894835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03071
26.9%
1505
 
4.4%
0.928571429184
 
1.6%
0.5126
 
1.1%
0.80952381113
 
1.0%
0.33333333398
 
0.9%
0.14285714398
 
0.9%
0.66666666797
 
0.8%
0.2580
 
0.7%
0.81818181866
 
0.6%
Other values (3121)6992
61.2%
ValueCountFrequency (%)
03071
26.9%
0.0006406151
 
< 0.1%
0.0015384621
 
< 0.1%
0.0022042621
 
< 0.1%
0.0023273861
 
< 0.1%
0.0027347311
 
< 0.1%
0.003091191
 
< 0.1%
0.003129891
 
< 0.1%
0.0031347961
 
< 0.1%
0.0031974421
 
< 0.1%
ValueCountFrequency (%)
1505
4.4%
0.9911504421
 
< 0.1%
0.9901960781
 
< 0.1%
0.9880715715
 
< 0.1%
0.9843751
 
< 0.1%
0.9835589941
 
< 0.1%
0.9833333331
 
< 0.1%
0.9827586211
 
< 0.1%
0.9789915971
 
< 0.1%
0.9787234041
 
< 0.1%

ratio_nullHyperlinks
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011430
100.0%

Length

2022-01-23T16:47:52.105062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:52.163886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011430
100.0%

Most occurring characters

ValueCountFrequency (%)
011430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011430
100.0%

nb_extCSS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.784864392
Minimum0
Maximum124
Zeros7828
Zeros (%)68.5%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:52.230141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum124
Range124
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.758801909
Coefficient of variation (CV)3.51500455
Kurtosis887.2521011
Mean0.784864392
Median Absolute Deviation (MAD)0
Skewness23.49547911
Sum8971
Variance7.610987973
MonotonicityNot monotonic
2022-01-23T16:47:52.346156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
07828
68.5%
11860
 
16.3%
2723
 
6.3%
3466
 
4.1%
4164
 
1.4%
5119
 
1.0%
676
 
0.7%
735
 
0.3%
829
 
0.3%
927
 
0.2%
Other values (23)103
 
0.9%
ValueCountFrequency (%)
07828
68.5%
11860
 
16.3%
2723
 
6.3%
3466
 
4.1%
4164
 
1.4%
5119
 
1.0%
676
 
0.7%
735
 
0.3%
829
 
0.3%
927
 
0.2%
ValueCountFrequency (%)
1241
< 0.1%
1231
< 0.1%
951
< 0.1%
712
< 0.1%
381
< 0.1%
312
< 0.1%
271
< 0.1%
251
< 0.1%
241
< 0.1%
231
< 0.1%

ratio_intRedirection
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011430
100.0%

Length

2022-01-23T16:47:52.540663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:52.600997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011430
100.0%

Most occurring characters

ValueCountFrequency (%)
011430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011430
100.0%

ratio_extRedirection
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct894
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1589256155
Minimum0
Maximum2
Zeros6143
Zeros (%)53.7%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:52.682214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.230769231
95-th percentile0.7809610984
Maximum2
Range2
Interquartile range (IQR)0.230769231

Descriptive statistics

Standard deviation0.2664370492
Coefficient of variation (CV)1.676489019
Kurtosis6.639212801
Mean0.1589256155
Median Absolute Deviation (MAD)0
Skewness2.296810119
Sum1816.519785
Variance0.07098870119
MonotonicityNot monotonic
2022-01-23T16:47:52.815056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06143
53.7%
1290
 
2.5%
0.5207
 
1.8%
0.333333333164
 
1.4%
0.25159
 
1.4%
0.285714286145
 
1.3%
0.846153846131
 
1.1%
0.2121
 
1.1%
0.166666667118
 
1.0%
0.125106
 
0.9%
Other values (884)3846
33.6%
ValueCountFrequency (%)
06143
53.7%
0.0024752481
 
< 0.1%
0.0042372881
 
< 0.1%
0.0045248871
 
< 0.1%
0.0063694271
 
< 0.1%
0.0065359481
 
< 0.1%
0.0066666671
 
< 0.1%
0.0068027211
 
< 0.1%
0.0068493153
 
< 0.1%
0.0069444441
 
< 0.1%
ValueCountFrequency (%)
221
0.2%
1.9221967961
 
< 0.1%
1.8108108111
 
< 0.1%
1.58
 
0.1%
1.4545454551
 
< 0.1%
1.4444444441
 
< 0.1%
1.4193548391
 
< 0.1%
1.4117647061
 
< 0.1%
1.3333333332
 
< 0.1%
1.2948717951
 
< 0.1%

ratio_intErrors
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011430
100.0%

Length

2022-01-23T16:47:53.023919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:53.081686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011430
100.0%

Most occurring characters

ValueCountFrequency (%)
011430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011430
100.0%

ratio_extErrors
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct635
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06246862847
Minimum0
Maximum1
Zeros8121
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:53.158895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.034482759
95-th percentile0.4
Maximum1
Range1
Interquartile range (IQR)0.034482759

Descriptive statistics

Standard deviation0.1562086786
Coefficient of variation (CV)2.500594016
Kurtosis13.62793409
Mean0.06246862847
Median Absolute Deviation (MAD)0
Skewness3.510797527
Sum714.0164234
Variance0.02440115125
MonotonicityNot monotonic
2022-01-23T16:47:53.293997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08121
71.0%
0.615384615130
 
1.1%
0.25103
 
0.9%
0.142857143101
 
0.9%
0.297
 
0.8%
0.16666666790
 
0.8%
0.12585
 
0.7%
185
 
0.7%
0.08333333376
 
0.7%
0.33333333375
 
0.7%
Other values (625)2467
 
21.6%
ValueCountFrequency (%)
08121
71.0%
0.002288331
 
< 0.1%
0.0042372881
 
< 0.1%
0.0042918451
 
< 0.1%
0.0043668121
 
< 0.1%
0.0046728971
 
< 0.1%
0.0047169811
 
< 0.1%
0.0049261081
 
< 0.1%
0.0049504951
 
< 0.1%
0.0054347831
 
< 0.1%
ValueCountFrequency (%)
185
0.7%
0.9791666672
 
< 0.1%
0.951
 
< 0.1%
0.9333333331
 
< 0.1%
0.91
 
< 0.1%
0.8754
 
< 0.1%
0.8571428571
 
< 0.1%
0.8518518521
 
< 0.1%
0.8461538461
 
< 0.1%
0.842
 
< 0.1%

login_form
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10703 
1
 
727

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

Length

2022-01-23T16:47:53.491278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:53.555271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

Most occurring characters

ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

external_favicon
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
6376 
1
5054 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06376
55.8%
15054
44.2%

Length

2022-01-23T16:47:53.720349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:53.778521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
06376
55.8%
15054
44.2%

Most occurring characters

ValueCountFrequency (%)
06376
55.8%
15054
44.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06376
55.8%
15054
44.2%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06376
55.8%
15054
44.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06376
55.8%
15054
44.2%

links_in_tags
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct473
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.97821079
Minimum0
Maximum100
Zeros3403
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:53.861080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median60
Q398.06100357
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)98.06100357

Descriptive statistics

Standard deviation41.52314377
Coefficient of variation (CV)0.7988567351
Kurtosis-1.667946113
Mean51.97821079
Median Absolute Deviation (MAD)40
Skewness-0.1507697409
Sum594110.9493
Variance1724.171468
MonotonicityNot monotonic
2022-01-23T16:47:53.991187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03403
29.8%
1002851
24.9%
50453
 
4.0%
66.66666667345
 
3.0%
75195
 
1.7%
33.33333333169
 
1.5%
80138
 
1.2%
60129
 
1.1%
83.33333333114
 
1.0%
81.81818182106
 
0.9%
Other values (463)3527
30.9%
ValueCountFrequency (%)
03403
29.8%
0.2087682671
 
< 0.1%
0.2092050211
 
< 0.1%
1.3698630141
 
< 0.1%
1.4084507041
 
< 0.1%
1.56251
 
< 0.1%
1.7543859651
 
< 0.1%
1.8867924531
 
< 0.1%
1.9417475731
 
< 0.1%
1.9607843146
 
0.1%
ValueCountFrequency (%)
1002851
24.9%
99.047619051
 
< 0.1%
98.571428571
 
< 0.1%
98.275862071
 
< 0.1%
98.181818182
 
< 0.1%
98.148148151
 
< 0.1%
98.076923081
 
< 0.1%
98.013245031
 
< 0.1%
97.916666671
 
< 0.1%
97.872340431
 
< 0.1%

submit_email
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011430
100.0%

Length

2022-01-23T16:47:54.191374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:54.251523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011430
100.0%

Most occurring characters

ValueCountFrequency (%)
011430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011430
100.0%

ratio_intMedia
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct490
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.87044363
Minimum0
Maximum100
Zeros5469
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:54.330364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.11111111
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation46.24989655
Coefficient of variation (CV)1.078829436
Kurtosis-1.82129959
Mean42.87044363
Median Absolute Deviation (MAD)11.11111111
Skewness0.2758129688
Sum490009.1707
Variance2139.05293
MonotonicityNot monotonic
2022-01-23T16:47:54.461380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05469
47.8%
1003461
30.3%
50268
 
2.3%
20149
 
1.3%
80131
 
1.1%
33.33333333110
 
1.0%
66.66666667108
 
0.9%
16.6666666785
 
0.7%
7575
 
0.7%
83.3333333370
 
0.6%
Other values (480)1504
 
13.2%
ValueCountFrequency (%)
05469
47.8%
0.5813953491
 
< 0.1%
0.6557377051
 
< 0.1%
0.8771929821
 
< 0.1%
0.9090909091
 
< 0.1%
0.9345794391
 
< 0.1%
0.9803921573
 
< 0.1%
11
 
< 0.1%
1.1363636361
 
< 0.1%
1.2987012993
 
< 0.1%
ValueCountFrequency (%)
1003461
30.3%
99.785867241
 
< 0.1%
99.69788521
 
< 0.1%
99.658703071
 
< 0.1%
99.651567941
 
< 0.1%
99.602385691
 
< 0.1%
99.494949492
 
< 0.1%
99.492385791
 
< 0.1%
99.479166671
 
< 0.1%
99.457994581
 
< 0.1%

ratio_extMedia
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct490
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.23629302
Minimum0
Maximum100
Zeros7335
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:54.592560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q333.33333333
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)33.33333333

Descriptive statistics

Standard deviation38.38657665
Coefficient of variation (CV)1.652009493
Kurtosis-0.1957378857
Mean23.23629302
Median Absolute Deviation (MAD)0
Skewness1.265615285
Sum265590.8293
Variance1473.529267
MonotonicityNot monotonic
2022-01-23T16:47:54.726362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07335
64.2%
1001595
 
14.0%
50268
 
2.3%
80149
 
1.3%
20131
 
1.1%
66.66666667110
 
1.0%
33.33333333108
 
0.9%
83.3333333385
 
0.7%
2575
 
0.7%
16.6666666770
 
0.6%
Other values (480)1504
 
13.2%
ValueCountFrequency (%)
07335
64.2%
0.2141327621
 
< 0.1%
0.3021148041
 
< 0.1%
0.3412969281
 
< 0.1%
0.3484320561
 
< 0.1%
0.3976143141
 
< 0.1%
0.5050505052
 
< 0.1%
0.5076142131
 
< 0.1%
0.5208333331
 
< 0.1%
0.542005421
 
< 0.1%
ValueCountFrequency (%)
1001595
14.0%
99.418604651
 
< 0.1%
99.34426231
 
< 0.1%
99.122807021
 
< 0.1%
99.090909091
 
< 0.1%
99.065420561
 
< 0.1%
99.019607843
 
< 0.1%
991
 
< 0.1%
98.863636361
 
< 0.1%
98.70129873
 
< 0.1%

sfh
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011430
100.0%

Length

2022-01-23T16:47:54.933559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:54.993114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011430
100.0%

Most occurring characters

ValueCountFrequency (%)
011430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011430
100.0%

iframe
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11415 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

Length

2022-01-23T16:47:55.143951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:55.205270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

Most occurring characters

ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

popup_window
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11361 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

Length

2022-01-23T16:47:55.363603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:55.424594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

Most occurring characters

ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

safe_anchor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1083
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.06392173
Minimum0
Maximum100
Zeros4438
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:55.509282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23.29457364
Q375
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)75

Descriptive statistics

Standard deviation39.07338516
Coefficient of variation (CV)1.054216158
Kurtosis-1.349056923
Mean37.06392173
Median Absolute Deviation (MAD)23.29457364
Skewness0.5130752182
Sum423640.6254
Variance1526.729428
MonotonicityNot monotonic
2022-01-23T16:47:55.639238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04438
38.8%
1001732
 
15.2%
50337
 
2.9%
25319
 
2.8%
14.28571429225
 
2.0%
33.33333333198
 
1.7%
66.66666667156
 
1.4%
20128
 
1.1%
60105
 
0.9%
75102
 
0.9%
Other values (1073)3690
32.3%
ValueCountFrequency (%)
04438
38.8%
0.179211471
 
< 0.1%
0.645161291
 
< 0.1%
0.7299270071
 
< 0.1%
0.7462686571
 
< 0.1%
0.7751937981
 
< 0.1%
0.9090909091
 
< 0.1%
1.1363636361
 
< 0.1%
1.2345679011
 
< 0.1%
1.251
 
< 0.1%
ValueCountFrequency (%)
1001732
15.2%
99.818840581
 
< 0.1%
99.751243781
 
< 0.1%
99.722222221
 
< 0.1%
99.650145771
 
< 0.1%
99.622166251
 
< 0.1%
99.549549551
 
< 0.1%
99.530127141
 
< 0.1%
99.523809521
 
< 0.1%
99.310344831
 
< 0.1%

onmouseover
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11417 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

Length

2022-01-23T16:47:55.841913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:55.902758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

Most occurring characters

ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

right_clic
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11414 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

Length

2022-01-23T16:47:56.062534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:56.125942image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

Most occurring characters

ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

empty_title
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10004 
1
1426 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

Length

2022-01-23T16:47:56.279744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:56.341646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

Most occurring characters

ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

domain_in_title
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
1
8868 
0
2562 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

Length

2022-01-23T16:47:56.506935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:56.568404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

Most occurring characters

ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

domain_with_copyright
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
6406 
1
5024 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
06406
56.0%
15024
44.0%

Length

2022-01-23T16:47:56.736665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:56.798868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
06406
56.0%
15024
44.0%

Most occurring characters

ValueCountFrequency (%)
06406
56.0%
15024
44.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06406
56.0%
15024
44.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06406
56.0%
15024
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06406
56.0%
15024
44.0%

whois_registered_domain
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10597 
1
 
833

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

Length

2022-01-23T16:47:56.954556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:57.016204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

Most occurring characters

ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

domain_registration_length
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1659
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean492.532196
Minimum-1
Maximum29829
Zeros1404
Zeros (%)12.3%
Negative46
Negative (%)0.4%
Memory size89.4 KiB
2022-01-23T16:47:57.098966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q184
median242
Q3449
95-th percentile2430
Maximum29829
Range29830
Interquartile range (IQR)365

Descriptive statistics

Standard deviation814.7694152
Coefficient of variation (CV)1.654246
Kurtosis294.6779341
Mean492.532196
Median Absolute Deviation (MAD)163
Skewness9.819607445
Sum5629643
Variance663849.1999
MonotonicityNot monotonic
2022-01-23T16:47:57.225400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01404
 
12.3%
25270
 
2.4%
374208
 
1.8%
228117
 
1.0%
21789
 
0.8%
37370
 
0.6%
37163
 
0.6%
1459
 
0.5%
90259
 
0.5%
12258
 
0.5%
Other values (1649)9033
79.0%
ValueCountFrequency (%)
-146
 
0.4%
01404
12.3%
15
 
< 0.1%
28
 
0.1%
38
 
0.1%
49
 
0.1%
57
 
0.1%
67
 
0.1%
717
 
0.1%
85
 
< 0.1%
ValueCountFrequency (%)
298291
< 0.1%
297251
< 0.1%
71021
< 0.1%
36211
< 0.1%
36111
< 0.1%
35911
< 0.1%
35711
< 0.1%
35691
< 0.1%
35681
< 0.1%
35671
< 0.1%

domain_age
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4430
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4062.543745
Minimum-12
Maximum12874
Zeros6
Zeros (%)0.1%
Negative1837
Negative (%)16.1%
Memory size89.4 KiB
2022-01-23T16:47:57.356533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile-1
Q1972.25
median3993
Q37026.75
95-th percentile8759
Maximum12874
Range12886
Interquartile range (IQR)6054.5

Descriptive statistics

Standard deviation3107.7846
Coefficient of variation (CV)0.7649848952
Kurtosis-1.097304785
Mean4062.543745
Median Absolute Deviation (MAD)3025.5
Skewness0.1641867389
Sum46434875
Variance9658325.123
MonotonicityNot monotonic
2022-01-23T16:47:57.492682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11781
 
15.6%
7295197
 
1.7%
3993156
 
1.4%
3992117
 
1.0%
561673
 
0.6%
400370
 
0.6%
562767
 
0.6%
729667
 
0.6%
834862
 
0.5%
713360
 
0.5%
Other values (4420)8780
76.8%
ValueCountFrequency (%)
-121
 
< 0.1%
-255
 
0.5%
-11781
15.6%
06
 
0.1%
124
 
0.2%
213
 
0.1%
34
 
< 0.1%
47
 
0.1%
514
 
0.1%
63
 
< 0.1%
ValueCountFrequency (%)
128741
< 0.1%
128732
< 0.1%
128721
< 0.1%
128481
< 0.1%
128442
< 0.1%
128111
< 0.1%
128051
< 0.1%
127951
< 0.1%
127941
< 0.1%
127901
< 0.1%

web_traffic
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4744
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean856756.6433
Minimum0
Maximum10767986
Zeros4444
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:57.627799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1651
Q3373845.5
95-th percentile5707171
Maximum10767986
Range10767986
Interquartile range (IQR)373845.5

Descriptive statistics

Standard deviation1995606.022
Coefficient of variation (CV)2.329256548
Kurtosis7.306645061
Mean856756.6433
Median Absolute Deviation (MAD)1651
Skewness2.779269268
Sum9792728433
Variance3.982443394 × 1012
MonotonicityNot monotonic
2022-01-23T16:47:57.771326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04444
38.9%
5707171176
 
1.5%
12163
 
1.4%
1148
 
1.3%
569797691
 
0.8%
581661758
 
0.5%
242
 
0.4%
838
 
0.3%
2221137
 
0.3%
436536
 
0.3%
Other values (4734)6197
54.2%
ValueCountFrequency (%)
04444
38.9%
1148
 
1.3%
242
 
0.4%
41
 
< 0.1%
838
 
0.3%
111
 
< 0.1%
12163
 
1.4%
1319
 
0.2%
154
 
< 0.1%
161
 
< 0.1%
ValueCountFrequency (%)
107679861
< 0.1%
107499991
< 0.1%
107457221
< 0.1%
107449761
< 0.1%
107252451
< 0.1%
107182271
< 0.1%
106968101
< 0.1%
106877671
< 0.1%
106842841
< 0.1%
106759271
< 0.1%

dns_record
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11200 
1
 
230

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

Length

2022-01-23T16:47:57.985316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:58.049162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

Most occurring characters

ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

google_index
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
1
6103 
0
5327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
16103
53.4%
05327
46.6%

Length

2022-01-23T16:47:58.226709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:58.291816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
16103
53.4%
05327
46.6%

Most occurring characters

ValueCountFrequency (%)
16103
53.4%
05327
46.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
16103
53.4%
05327
46.6%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
16103
53.4%
05327
46.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16103
53.4%
05327
46.6%

page_rank
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.185739283
Minimum0
Maximum10
Zeros2666
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2022-01-23T16:47:58.355763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.53695541
Coefficient of variation (CV)0.7963474675
Kurtosis-0.3863146639
Mean3.185739283
Median Absolute Deviation (MAD)2
Skewness0.4460310293
Sum36413
Variance6.436142751
MonotonicityNot monotonic
2022-01-23T16:47:58.450008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
02666
23.3%
52057
18.0%
21553
13.6%
41380
12.1%
31232
10.8%
1735
 
6.4%
6727
 
6.4%
7509
 
4.5%
10269
 
2.4%
8262
 
2.3%
ValueCountFrequency (%)
02666
23.3%
1735
 
6.4%
21553
13.6%
31232
10.8%
41380
12.1%
52057
18.0%
6727
 
6.4%
7509
 
4.5%
8262
 
2.3%
940
 
0.3%
ValueCountFrequency (%)
10269
 
2.4%
940
 
0.3%
8262
 
2.3%
7509
 
4.5%
6727
 
6.4%
52057
18.0%
41380
12.1%
31232
10.8%
21553
13.6%
1735
 
6.4%

status
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
legitimate
5715 
phishing
5715 

Length

Max length10
Median length9
Mean length9
Min length8

Characters and Unicode

Total characters102870
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlegitimate
2nd rowphishing
3rd rowphishing
4th rowlegitimate
5th rowlegitimate

Common Values

ValueCountFrequency (%)
legitimate5715
50.0%
phishing5715
50.0%

Length

2022-01-23T16:47:58.663957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-23T16:47:58.740072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
legitimate5715
50.0%
phishing5715
50.0%

Most occurring characters

ValueCountFrequency (%)
i22860
22.2%
e11430
11.1%
g11430
11.1%
t11430
11.1%
h11430
11.1%
l5715
 
5.6%
m5715
 
5.6%
a5715
 
5.6%
p5715
 
5.6%
s5715
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter102870
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i22860
22.2%
e11430
11.1%
g11430
11.1%
t11430
11.1%
h11430
11.1%
l5715
 
5.6%
m5715
 
5.6%
a5715
 
5.6%
p5715
 
5.6%
s5715
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin102870
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i22860
22.2%
e11430
11.1%
g11430
11.1%
t11430
11.1%
h11430
11.1%
l5715
 
5.6%
m5715
 
5.6%
a5715
 
5.6%
p5715
 
5.6%
s5715
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII102870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i22860
22.2%
e11430
11.1%
g11430
11.1%
t11430
11.1%
h11430
11.1%
l5715
 
5.6%
m5715
 
5.6%
a5715
 
5.6%
p5715
 
5.6%
s5715
 
5.6%

Interactions

2022-01-23T16:46:13.048255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:13.187107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:13.311074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:13.433730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:13.561430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:13.685684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:13.814638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:13.944067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:14.061423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:14.182208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:14.300255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:14.421683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:14.577917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:14.696747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:15.189435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:15.318841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:15.447066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:15.691443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:15.817133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:16.070385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:16.196849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:16.327481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:16.454801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:16.580713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:16.702643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:16.826998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:16.946719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:17.065694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:17.189195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:17.310515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:17.439018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:17.564734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:17.681072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:17.803170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:17.921101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:18.045299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:18.171733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:18.290621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:18.760167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:18.879117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:19.006255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:19.245110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:19.362034image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:19.478510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:19.714281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:19.845756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:19.966816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:20.084861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:20.216168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:20.332090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:20.446717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:20.563649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:20.682570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:20.809899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:20.939675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:21.067326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:21.188224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:21.310999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:21.423477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:21.549338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:21.671312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:21.785798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:22.250057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:22.365550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:22.489076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:22.716068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:22.830007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:22.944875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:23.060444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:23.178562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:23.295426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:23.410392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:23.522502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:23.643128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:23.765811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:23.885568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:24.165840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:24.300143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:24.429837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:24.557674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:24.677517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:24.799938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:24.919384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:25.046703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:25.175712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:25.296809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:25.811726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:25.936388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:26.068713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:26.313010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:26.433346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:26.552867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:26.672329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:26.799495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:26.924649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:27.045699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:27.164618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:27.285486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:27.403399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:27.523202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:27.645669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:27.765689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:27.889183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:28.013685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:28.131856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:28.251513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:28.368918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:28.489294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:28.614053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:28.730895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:29.202060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:29.325666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:29.640507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:29.890475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:30.008650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:30.127664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:30.247252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:30.374522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:30.498203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:30.620369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:30.738967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:30.867048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:30.998777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:31.125040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:31.253375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:31.382343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:31.513918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:31.651665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:31.775633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:31.901842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:32.019757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:32.138958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:32.262103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:32.383510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:46:32.842512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-01-23T16:47:36.144082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:36.238485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:36.335968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:36.429116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:36.528835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:36.632004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:36.734596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:37.130006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:37.231566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:37.338065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:37.540372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:37.638998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:37.740125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:37.835881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:37.936765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:38.034875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:38.132196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:38.226416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:38.320053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:38.413941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:38.507155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:38.606270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:38.702187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:38.801553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:38.901514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:38.991345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:39.087747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:39.178589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:39.274268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:39.372666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:39.465669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:39.843015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:39.938617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:40.046237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:40.239236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:40.334428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:40.429944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:40.523751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:40.621971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:40.717058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-23T16:47:40.813760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-01-23T16:47:58.941259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-23T16:48:00.242909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-23T16:48:01.561662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-23T16:48:02.875205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-01-23T16:48:04.040840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-01-23T16:47:41.314378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

urlipnb_wwwnb_comnb_dslashhttp_in_pathpunycodeporttld_in_pathtld_in_subdomainabnormal_subdomainnb_subdomainsprefix_suffixrandom_domainshortening_servicepath_extensionnb_redirectionnb_external_redirectionlength_words_rawchar_repeatshortest_words_rawshortest_word_hostshortest_word_pathlongest_words_rawlongest_word_hostlongest_word_pathavg_words_rawavg_word_hostavg_word_pathphish_hintsdomain_in_brandbrand_in_subdomainbrand_in_pathsuspecious_tldstatistical_reportnb_hyperlinksratio_intHyperlinksratio_extHyperlinksratio_nullHyperlinksnb_extCSSratio_intRedirectionratio_extRedirectionratio_intErrorsratio_extErrorslogin_formexternal_faviconlinks_in_tagssubmit_emailratio_intMediaratio_extMediasfhiframepopup_windowsafe_anchoronmouseoverright_clicempty_titledomain_in_titledomain_with_copyrightwhois_registered_domaindomain_registration_lengthdomain_ageweb_trafficdns_recordgoogle_indexpage_rankstatus
0http://www.crestonwood.com/router.php0100000000300000044333111165.7500007.04.500000000000170.5294120.4705880000.87500000.5000000080.0000000100.0000000.0000000000.00000000001045-10114legitimate
1http://shadetreetechnology.com/V4/validation/a111aedc8ae390eabcfa130e041a10a41000000000100001044219232193215.75000019.014.666667000000300.9666670.0333330000.00000000.00000000100.000000080.00000020.000000000100.0000000001007757670012phishing
2https://support-appleld.com.secureupdate.duilawyeryork.com/ap/89e6a3b4b063b8d/?cmd=_update&dispatch=89e6a3b4b063b8d1b&locale=_101000001031000101222321713178.2500008.48.14285700000041.0000000.0000000000.00000000.00000000100.00000000.0000000.000000000100.0000000001001440045828815010phishing
3http://rgipt.ac.in00000000002000010105505505.0000005.00.0000000000001490.9731540.0268460000.25000000.25000000100.000000096.4285713.57142900062.50000000010062-1107721003legitimate
4http://www.iracing.com/tracks/gateway-motorsports-park/0100000000200001063334117116.3333335.07.0000000000001020.4705880.5294120000.53703700.0185191076.47058800.000000100.0000000000.00000000001022481758725006legitimate
5http://appleid.apple.com-app.es/00100000003100010433307704.5000004.50.000000000000100.3000000.7000000000.57142900.00000000100.00000000.0000000.0000000000.0000000001110-10010phishing
6http://www.mutuo.it01000000002000010233305504.0000004.00.000000000000980.0816330.91836701000.00000000.000000010.00000000.000000100.000000000100.00000000001017075290001legitimate
7http://www.shadetreetechnology.com/V4/validation/ba4b8bddd7958ecb8772c836c2969531110000000020000105823232193213.20000011.014.666667000000300.9666670.0333330000.00000000.00000000100.000000080.00000020.000000000100.0000000001007657670012phishing
8http://vamoaestudiarmedicina.blogspot.com/00000000002001000208802121014.50000014.50.000000010000630.2063490.7936510300.38000000.000000010.00000000.000000100.00000000027.27272700011037172980005legitimate
9https://parade.com/425836/joshwigler/the-amazing-race-host-phil-keoghan-previews-the-season-27-premiere/00000000001000000140262106105.5714296.05.5384621000001400.7785710.2214290100.19354800.0000000193.103448010.00000090.00000000058.13953500010012893686774005legitimate

Last rows

urlipnb_wwwnb_comnb_dslashhttp_in_pathpunycodeporttld_in_pathtld_in_subdomainabnormal_subdomainnb_subdomainsprefix_suffixrandom_domainshortening_servicepath_extensionnb_redirectionnb_external_redirectionlength_words_rawchar_repeatshortest_words_rawshortest_word_hostshortest_word_pathlongest_words_rawlongest_word_hostlongest_word_pathavg_words_rawavg_word_hostavg_word_pathphish_hintsdomain_in_brandbrand_in_subdomainbrand_in_pathsuspecious_tldstatistical_reportnb_hyperlinksratio_intHyperlinksratio_extHyperlinksratio_nullHyperlinksnb_extCSSratio_intRedirectionratio_extRedirectionratio_intErrorsratio_extErrorslogin_formexternal_faviconlinks_in_tagssubmit_emailratio_intMediaratio_extMediasfhiframepopup_windowsafe_anchoronmouseoverright_clicempty_titledomain_in_titledomain_with_copyrightwhois_registered_domaindomain_registration_lengthdomain_ageweb_trafficdns_recordgoogle_indexpage_rankstatus
11420https://adnanboz.wordpress.com/2012/01/06/how-to-set-up-amazon-ec2-windows-gpu-instance-for-nvidia-cuda-development/00000000002000000181282119114.7777788.504.312500010000160.1875000.8125000400.00000000.0000000116.66666700.0000000.0000000000.00000000010058574490008legitimate
11421http://www.peoplemakingplaces.com/includes/Support/En/log/signin/customer_center/customer-IDPP00C644/myaccount/signin010000000021000101382321818107.23076910.506.636364400000420.8095240.1904760100.50000000.0000000150.0000000100.0000000.00000000020.00000000010013420580012phishing
11422http://sheetdownload.com/0000000000100001011131301313013.00000013.000.0000000000001390.9784170.0215830000.00000000.0000000080.0000000100.0000000.000000000100.0000000000001442778788648002legitimate
11423http://www.dmega.co.kr/dmega/data/qna/sec/page.php?email=ZmFpdGhAc2VtYW50aWMuaW5mbw==01000000003000000103333265266.1000004.006.625000000000921.0000000.0000000000.00000000.00000000100.0000000100.0000000.000000010100.000000000010293518610408014011phishing
11424http://www.answers.com/Q/What_are_the_sizes_of_computer_memory011000010020000101031318784.2000005.004.000000000000660.8484850.1515150100.00000000.0000000189.28571400.0000000.00000000020.00000000001050688292635016legitimate
11425http://www.fontspace.com/category/blackletter0100000000200001044338119117.7500006.009.5000000000001990.8844220.1155780000.04347800.1739130180.000000021.05263278.9473680000.00000000000044853963980006legitimate
11426http://www.budgetbots.com/server.php/Server%20update/index.php?email=USER@DOMAIN.com01100001003000010123333101085.1666676.504.90000000000031.0000000.0000000000.00000000.00000000100.00000000.0000000.0000000000.00000000010021167280010phishing
11427https://www.facebook.com/Interactive-Television-Pvt-Ltd-Group-M-100230523435650/photos/?ref=page_internal11000000002000000135131158156.1538465.506.272727010000680.4705880.5294120500.00000000.000000016.25000000.0000000.00000000080.0000000000002809851580110legitimate
11428http://www.mypublicdomainpictures.com/01000000002000000233302222012.50000012.500.000000000000320.3750000.6250000100.05000000.0500000116.66666700.000000100.0000000000.0000000001008528362455493004legitimate
11429http://174.139.46.123/ap/signin?openid.pape.max_auth_age=0&amp;openid.return_to=https%3A%2F%2Fwww.amazon.co.jp%2F%3Fref_%3Dnav_em_hd_re_signin&amp;openid.identity=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0%2Fidentifier_select&amp;openid.assoc_handle=jpflex&amp;openid.mode=checkid_setup&amp;key=a@b.c&amp;openid.claimed_id=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0%2Fidentifier_select&amp;openid.ns=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0&amp;&amp;ref_=nav_em_hd_clc_signin11004001113000011908121123124.3777782.754.453488300102210.4285710.5714290300.00000000.083333110.00000000.0000000.00000000033.3333330001110-10110phishing